Accurate biodiversity monitoring is essential for effective environmental policy, yet current practices often rely on arbitrarily defined ecosystems, communities, and ad-hoc indicator species, limiting cost-efficiency and reproducibility. We present a model-based framework that infers ecological sub-communities and corresponding indicators in terms of habitat and species from species survey data, such as large-scale arthropod abundance data used here as example. Environments and species are co-clustered using Bayesian decoupling for Poisson factorization. Latent, hierarchical regression relates observable habitat features to each subcommunity. Additionally, we propose a novel, model-based ranking of indicator species based on the learned subcommunities, generalizing classical approaches. This integrated approach motivates model-based ecosystem classification and indicator species selection, offering a scalable, reproducible pathway for biodiversity monitoring and informed conservation.

翻译：准确的生物多样性监测对于制定有效的环境政策至关重要，然而当前实践常依赖于任意定义的生态系统、群落以及临时指定的指示物种，这限制了成本效益与可重复性。我们提出一种基于模型的框架，该框架能够从物种调查数据（例如本文作为示例使用的大规模节肢动物丰度数据）中推断出生态子群落及其对应的生境与物种指示因子。通过贝叶斯解耦的泊松分解方法，环境与物种被共同聚类。潜在的分层回归模型将可观测的生境特征与每个子群落相关联。此外，我们基于学习到的子群落提出了一种新颖的、基于模型的指示物种排序方法，从而推广了经典途径。这一集成方法推动了基于模型的生态系统分类与指示物种选择，为生物多样性监测与科学保护提供了可扩展、可重复的路径。